Modellgestützte Prozeßleittechnik in der verfahrenstechnischen Produktion

Model-based process control engineering in chemical engineering production . Modern process control engineering nowadays provides almost ideal tools for the implementation of even highly sophisticated control concepts. The very reluctant acceptance of these tools is due to the following bottlenecks:

• – insufficient understanding of chemical engineering control processes;
• – limitations in the acquisition of sensorial primary information;
• – insufficient performance of control engineering concepts.

The present articles considers how these bottlenecks can be overcome.     

The triplet “molecular processes–product–process” engineering: the future of chemical engineering ?

Today chemical engineering has to answer to the changing needs of the chemical and related process industries and to meet the market demands. Being a key to survival in globalization of trade and competition, the evolution of chemical engineering is thus necessary. Its ability to cope with the scientific and technological problems encountered will be appraised in this paper. To satisfy both the markets requirements for specific end-use properties of products and the social and environmental constraints of the industrial-scale processes, it is shown that a necessary progress is coming via a multidisciplinary and a time and length multiscale approach. This will be obtained due to breakthroughs in molecular modelling, scientific instrumentation and related signal processing and powerful computational tools. For the future of chemical engineering four main objectives are concerned: (a) to increase productivity and selectivity through intelligent operations via intensification and multiscale control of processes; (b) to design novel equipment based on scientific principles and new methods of production: process intensification; (c) to extend chemical engineering methodology to product focussed engineering, i.e. manufacturing and synthesizing end-use properties required by the customer, which needs a triplet “molecular processes–product–process” engineering; (d) to implement multiscale application of computational chemical engineering modelling and simulation to real-life situations, from the molecular scale to the overall complex production scale.     

Process systems engineering tools in the pharmaceutical industry

The purpose of this paper is to provide a summary of the current state of the application of process systems engineering tools in the pharmaceutical industry. In this paper, we present the compiled results of an industrial questionnaire submitted to pharmaceutical industry professionals. The topics covered in the questionnaire include process analytics, process monitoring, plant-wide information systems, unit operation modeling, quality control, and process optimization. A futuristic view of what process systems engineering tools will enable the pharmaceutical industry will be also be presented. While the industry is regularly using the traditional Design of Experiments approach to identify key parameters and to define control spaces, these approaches result in passive control strategies that do not attempt to compensate for disturbances. Special new approaches are needed for batch processes due to their essential dependence on time-varying conditions. Lastly, we briefly describe a novel data driven modeling approach, called Design of Dynamic Experiments that enables the optimization of batch processes with respect to time-varying conditions through an example of a simulated chemical reaction process. Many more approaches of this type are needed for the calculation of the design and control spaces of the process, and the effective design of feedback systems.     

化学工程科学发展的回顾与思考

简述了化学工程科学发展的主要成果,重点介绍了近期发展的“产品工程”,化学工程中的尺度问题及化工过程“场”和“流”分析等方面的进展及其对化学工程科学内容的贡献;提出以“质量传递与转化”,“能量传递与转化”及“信息传递与转化”来描述现代化学工程学科体系。本文还对我国化工科学及产业发展进行了展望。     

Data envelopment analysis approach to targeting in sustainable chemical process design: Application to liquid fuels

This article presents a framework for combining data envelopment analysis with process systems engineering tools, aiming to improve the sustainability of chemical processes. Given a set of chemical processes, each characterized by performance indicators, the framework discriminates between efficient and inefficient processes in regard to these indicators. We develop an approach to quantifying the closest targets for an inefficient process to become efficient, while preventing unrealistic targets by accounting for thermodynamic limitations represented as mass and energy flow constraints. We demonstrate the capabilities of the framework by assessing a methanol production process with captured CO₂ and fossil-based H₂, in comparison to 10 alternatives. The methanol fuel is found to be suboptimal in comparison with other fuels. Making it competitive would require a significant (unrealistic in the short term) reduction in H₂ price. Alternatively, the methanol fuel could become competitive upon combining fossil-based H₂ with sustainably produced H₂ via wind-powered electrolysis. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16480 2019     

A tribute to professor Roger Sargent: Intellectual leader of process systems engineering

This article and this issue of the AIChE Journal, is a tribute to Professor Roger Sargent who, as pioneer and intellectual leader of process systems engineering, has had a profound impact on the discipline of chemical engineering. Spanning more than five decades, his work has provided a strong mathematical foundation to process systems engineering through the development of sophisticated mathematical and computational tools for the simulation, design, control, operation and optimization of chemical processes. In this article we first give a brief overview of his career that included several leadership positions and the establishment of the Centre for Process Systems Engineering (CPSE) at Imperial College London. We next review his research contributions in the areas of process modeling, differential algebraic systems, process dynamics and control, nonlinear optimization and optimal control, design under uncertainty, and process scheduling. We highlight the tremendous impact that he has had through his students, students' students, and his entire academic family tree, which at present contains over 2000 names, probably one of the largest among the academic leaders of chemical engineering. Finally, we provide a brief overview of him as a modest and charming individual with a wonderful sense of humor. He is without doubt a true intellectual giant who has helped to expand the scope of chemical engineering by providing a strong systems component to it, and by establishing strong multidisciplinary links with other fields. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2951–2958, 2016     

Experimental methods in chemical engineering: Artificial neural networks–ANNs

Artificial neural networks (ANNs) are one of the most powerful and versatile tools provided by artificial intelligence and they have now been exploited by chemical engineers for several decades in countless applications. ANNs are computational tools providing a minimalistic mathematical model of neural functions. Coupled with raw data and a learning algorithm, they can be applied to tasks such as modelling, classification, and prediction. Recently, their popularity has grown remarkably and they now constitute one of the most relevant research areas within the fields of artificial intelligence and machine learning. ANNs are large collections of simple classifiers called neurons. Chemical engineers apply them to model complex relationships, predict reactor performance, and to automate process controllers. ANNs can leverage their ability to learn and exploit large data sets, but they can also get stuck in local minima or overfit and are difficult to reverse engineer. In 2016 and 2017, ANNs were cited in 13 245 Web of Science (WoS) articles, 538 of which were in chemical engineering; the top WoS categories were electrical & electronic engineering (1615 occurrences) artificial intelligence (1253), and energy & fuels (980). The top 4 journals mentioning ANNs were Neural Computing & Applications (117), Neurocomputing (84), Energies (76), and Renewable & Sustainable Energy Reviews (76). In the near future, as larger data sets become available (and arduous to analyze), chemical engineers will be able to apply and leverage more sophisticated ANN architectures.     

Information technology support in the chemical process design life cycle

Information technology has been becoming increasingly important in all areas of engineering during the last few years. Much of the progress achieved in chemical engineering would not have been possible without the enabling methods and tools provided by information technology. This trend will continue in the future but most likely with a considerably wider scope. While individual software tools and services have been in focus until recently, their integration into engineering work processes is an emerging and challenging area of research and development. This contribution attempts to highlight state of the art and future trends in supporting the activities during the life cycle of a chemical process by means of information technology. Emphasis will be largely on the process and plant design process rather than on procurement, manufacturing, and distribution of materials in the supply chain.     

Population balances for particulate processes—a volume approach

Population balance models have been used in chemical engineering since the 1960s and have evolved to become the most important tools for design and control of particulate processes. In this paper we show that the intrinsic particle parameter that determines changes in the process and should thus be included in the population balance is the particle volume. The basic population that is modeled should be the mass distribution, or the volume distribution if the density is constant. The population balance thus describes the change of the volume distribution of volume with time. Furthermore, we suggest that the “birth” and “death” terms that are often used to describe discrete events in particulate processes can almost always be replaced by a rate of change term.To design and control existing and future processes, a multi-dimensional population balance model is required. We propose a volume-based model in which the particle properties that are modeled are the volumes of solid, liquid, and air, respectively. In the most general case the model will consist of a properties vector and a distribution tensor. Depending on the complexity of the process, one or more of the properties may be omitted from the model. This is shown in three examples of increasing complexity: comminution, sintering, and granulation.     

Among the trends for a modern chemical engineering,the third paradigm: The time and length multiscale approach as an efficient tool for process intensification and product design and engineering

To respond to the changing needs of the chemical and related industries in order both to meet today's economy demands and to remain competitive in global trade, a modern chemical engineering is vital to satisfy both the market requirements for specific nano and microscale end-use properties of products, and the social and environmental constraints of industrial meso and macroscale processes. Thus an integrated system approach of complex multidisciplinary, non-linear, non-equilibrium processes and phenomena occurring on different length and time scales of the supply chain is required. That is, a good understanding of how phenomena at a smaller length-scale relates to properties and behaviour at a longer length-scale is necessary (from the molecular-scale to the production-scales). This has been defined as the triplet “molecular Processes-Product-Process (3PE)” integrated multiscale approach of chemical engineering. Indeed a modern chemical engineering can be summarized by four main objectives: (1) Increase productivity and selectivity through intensification of intelligent operations and a multiscale approach to processes control: nano and micro-tailoring of materials with controlled structure. (2) Design novel equipment based on scientific principles and new production methods: process intensification using multifunctional reactors and micro-engineering for micro structured equipment. (3) Manufacturing end-use properties to synthesize structured products, combining several functions required by the customer with a special emphasis on complex fluids and solid technology, necessating molecular modeling, polymorph prediction and sensor development. (4) Implement multiscale application of computational chemical engineering modeling and simulation to real-life situations from the molecular-scale to the production-scale, e.g., in order to understand how phenomena at a smaller length-scale relate to properties and behaviour at a longer length-scale. The presentation will emphasize the 3PE multiscale approach of chemical engineering for investigations in the previous objectives and on its success due to the today's considerable progress in the use of scientific instrumentation, in modeling, simulation and computer-aided tools, and in the systematic design methods.     

A Hybrid Method for Stochastic Performance Modeling and Optimization of Chemical Engineering Processes

As chemical engineers seek to improve plant safety, reliability, and financial performance, a wide range of uncertaintyladen decisions need to be made. It is widely agreed that probabilistic approaches provide a rational framework to quantify such uncertainties and can result in improved decision making and performance when compared with deterministic approaches. This article proposes a novel method for design and performance analysis of chemical engineering processes under uncertainty. The framework combines process simulation tools, response surface techniques, and numerical integration schemes applied in structural reliability problems to determine the probability of a process achieving a performance function of interest. The approach can be used to model processes in the presence or absence of performance function(s), with or without parameter interactions, at both design and operational phases. With this, process behavior can be quantified in terms of stochastic performance measures such as reliability indices and the associated most probable process design/operating conditions, providing a simple way to analyze a wide range of decisions. To validate the applicability of the proposed framework, three case study systems are considered: a plug flow reactor, a heat exchanger, and finally a pump system. In each case, performance criteria based on the original physical model and the surrogate model are set up. Reliability analysis is then carried out based on these two models and the results are assessed. The results show that the proposed framework can be successfully applied in chemical engineering analysis with additional benefits over the traditional deterministic methods.     

Multi-dimensional design process management by extended product modeling for concurrent process engineering

Conventional product and process models have focused on static features. That means product models are mainly based on structural decomposition of products, and process models are also often described by activity decomposition such as work breakdown structure. From the view of design process management, it is difficult to describe dynamic features of design processes appropriately through conventional methodologies. In this paper, a multidimensional approach for design process management was explored to manifest characteristics of design processes for chemical plant design. Parallelized design process for concurrent process engineering should be managed by twodimensional design activity flows. The process management makes it possible to guide progress of design processes in a helix structure by horizontal and vertical activity control simultaneously. They stand for teleological and causal relation between design activities, respectively. That can be achieved based on an extended product model, which represents various design perspectives explicitly from a conventional design activity model. The extended product model is composed of product data, design activities, and activity drivers. Dynamic features of the extended product model are expressed by an activity chain model. These concepts will support the realization of concurrent process engineering for chemical plant design in the sense that they provide design process management strategies.     

CAPE in der Verfahrenstechnik aus industrieller Sicht – Status,Bedarf, Prognose oder Vision?

CAPE in chemical engineering from an industrial viewpoint – Status, demands, outlook. The use of computers for solving chemical process problems is steadily gaining in importance. Simulation, design, optimization, and synthesis of processes are the main applications. The working group “Process simulation and process design” in the Dechema specialist committee “Use of computers in chemical engineering” has discussed the state of the art of simulation tools. Demands of industry on future tools have been outlined and a new simulator concept presented. If this concept is pursued, then interested companies will have to support development. The article presents background information and is intended to stimulate further interest.     

Process systems engineering: From Solvay to modern bio- and nanotechnology.: A history of development, successes and prospects for the future

The term Process Systems Engineering (PSE) is relatively recent. It was coined about 50 years ago at the outset of the modern era of computer-aided engineering. However, the engineering of processing systems is almost as old as the beginning of the chemical industry, around the first half of the 19th century. Initially, the practice of PSE was qualitative and informal, but as time went on it was formalized in progressively increasing degrees. Today, it is solidly founded on engineering sciences and an array of systems-theoretical methodologies and computer-aided tools. This paper is not a review of the theoretical and methodological contributions by various researchers in the area of PSE. Its primary objective is to provide an overview of the history of PSE, i.e. its origin and evolution; a brief illustration of its tremendous impact in the development of modern chemical industry; its state at the turn of the 21st century; and an outline of the role it can play in addressing the societal problems that we face today such as; securing sustainable production of energy, chemicals and materials for the human wellbeing, alternative energy sources, and improving the quality of life and of our living environment. PSE has expanded significantly beyond its original scope, the continuous and batch chemical processes and their associated process engineering problems. Today, PSE activities encompass the creative design, operation, and control of: biological systems (prokaryotic and eukaryotic cells); complex networks of chemical reactions; free or guided self-assembly processes; micro- and nano-scale processes; and systems that integrate engineered processes with processes driven by humans, legal and regulatory institutions. Through its emphasis on synthesis problems, PSE provides the dialectic complement to the analytical bent of chemical engineering science, thus establishing the healthy tension between synthesis and analysis, the foundation of any thriving discipline. As a consequence, throughout this paper PSE emerges as the foundational underpinning of modern chemical engineering; the one that ensures the discipline's cohesiveness in the years to come.     

The Effect of Correlated Kinetic Parameters on (Bio)Chemical Reaction Networks

Exploiting the information provided by (bio)chemical reaction networks has proved beneficial for process analysis and design. To this end, parameter uncertainties have to be included in the analysis and design of (bio)chemical processes to ensure reliable model‐based results. The goal is to investigate the impact of parameter correlations on (bio)chemical reaction networks and parameter sensitivities. An efficient sensitivity analysis concept is demonstrated with two simulation studies, and the results indicate a significant impact of the parameter correlations on the derived parameter sensitivities and the model‐based results in general.     

Multicomponent Fuzzy Model for Evaluating the Energy Efficiency of Chemical and Power Engineering Processes of Drying of the Multilayer Mass of Phosphorite Pellets

A multicomponent fuzzy model was proposed for evaluating the energy efficiency of the chemical and power engineering processes of the drying of a dynamic multilayer mass of phosphorite pellets in a complex multistage chemical and power engineering system (roasting conveyor machine). The developed model includes a set of fuzzy component models for analyzing the chemical and power engineering processes of pellet drying corresponding to the results of the decomposition of these processes, a set of neuro-fuzzy production models for evaluating the energy efficiency of the individual stages of the chemical and power engineering processes of pellet drying, and a neuro-fuzzy production model of generalized evaluation of the energy efficiency of the chemical and power engineering process of pellet drying. The use of the proposed model makes it possible to evaluate the energy efficiency of both the individual stages and, in general, the chemical and power engineering process of phosphorite pellet drying under conditions of uncertainty of their thermophysical characteristics and the processes themselves; to perform online structural adjustment and parametric adaptation of the model when the mode and chemical and power engineering process of pellet drying are changed; to perform online evaluation of the energy efficiency of the chemical and power engineering process of pellet drying; and to provide quality improvement and speed of decision making on optimization of the chemical and power engineering process of pellet drying to increase the energy efficiency of these processes.     

Next generation pure component property estimation models: With and without machine learning techniques

Physiochemical properties of pure components serve as the basis for the design and simulation of chemical products and processes. Models based on the molecular structural information of chemicals for the following 25 pure component properties are presented in this work: (critical-) temperature, pressure, volume, acentric factor; (normal-) boiling point, melting point, auto-ignition temperature; flash point; (standard-) enthalpy of formation, Gibbs energy of formation, enthalpy of fusion, enthalpy of vaporization, liquid molar volume; (environmental-) (lethal dose-) LC50 and LD50, photo-chemical oxidation potential, bioconcentration factor, permissible exposure limit; (physicochemical-) acid dissociation constant, water-solubility, octanol–water partition coefficient, Hildebrandt solubility parameter, Hansen solubility parameters. Utilizing functional groups for molecular representation, two parallel property estimation models where the group contributions for each property are regressed through traditional regression techniques and machine learning techniques are presented. Both techniques use an a priori data analysis before regression of model parameters. A dataset with more than 24,000 chemicals for the 25 pure component properties has been utilized for the development of the two sets of property models. The efficacy of the developed models and their use are highlighted together with a discussion on the overall performance, application range, and predictive capabilities with implications to product and/or process engineering problem solutions.     

Chemical engineering research synergies across scientific categories

Chemical engineers operate industrial plants, design reactors and equipment, manage capital projects, estimate costs, project earnings, and drive efficiency through innovation while maintaining rigorous safety standards. The undergraduate curriculum includes mathematics, physics, chemistry, mechanics, biology, and management, much of which is common with other engineering departments. 1 However, chemical engineering research is more related to chemistry. Here, we show that chemical engineers cite journals in WoS’ chemical engineering category most, followed by physical chemistry, energy & fuels, multi‐disciplinary chemistry, environmental science, and multi‐disciplinary materials science. According to a bibliometric analysis, the major research poles include materials, biotechnology, catalysis, environment, and thermodynamics. The 5 top cited journals in 2012 were Ind. Eng. Chem. Res., J. Membrane Sci., Chem. Eng. Sci., J. Hazard. Mater., and J. Catal., which are not the journals with the highest impact factors of the category. Can. J. Chem. Eng. was ranked third among the 32 classical chemical engineering journals after AIChE J. and Chem. Eng. Sci. and with respect to the ratio of the number of citations accrued until August 2017 to the number of articles they published in 2012. Chinese researchers have authored more articles than any other nation and they co‐author research most with the USA and other Pacific Rim nations. Research collaborations between nations follow linguistic, geographical, and historical traditions.

     

Membranen in der Verfahrenstechnik

Membranes in chemical engineering . In the last 10 years membranes and membrane processes have evolved from a useful laboratory tool to an industrial product of significant technical and commercial impact. In some applications membrane processes have today not only replaced some conventional separation procedures in the chemical process industry, because they are often more economical and yield better quality products, but they have also successfully been utilized to solve mass separation problems where conventional procedures failed or are too expensive. This contribution discusses the state of the art of membranes and membrane processes as well as their major applications, with the main emphasis being placed on more recent developments.